How to apply

The Centre invites applications from applicants with a background in any relevant discipline. Typically, applicants will be expected to hold a masters degree in a relevant subject area, or equivalent experience. Applicants would also need to be able to demonstrate a strong motivation for doing a PhD at the LCN. To undertake a PhD within the LCN, applicants will be registered at one of the three partner institutions.  

To apply for a PhD within the LCN it is first essential that you identify a potential supervisor and discuss with them your chosen area of research. You should review the Our People and Research pages, where you will find details of the research undertaken within the LCN and contact details for supervisors. Email is the preferred method of contacting a supervisor, and we suggest that you include, as a minimum, the following details in your initial email:

• Proposed area of research and your experience/background in the chosen research area
• How your chosen supervisor’s experience or knowledge is relevant
• A copy of your CV

Once you have agreement in principle from a supervisor that they would be happy to supervise you, please make a formal application to the relevant institution:

• Apply for a PhD at UCL via their Admissions Webpage 
• Apply for a PhD at Imperial College London via their Admission Webpage 
• Apply for a PhD at Kings College via their Admissions Webpage

PhD Opportunities

Here are all the available projects hosted at UCL in the London Centre for Nanotechnology (LCN) department:

2531bc1596 Developing microscopically informed qubit-level noise models in silicon

2531bc1597 Single-exposure Bragg Coherent Diffractive Imaging of Domains in Epitaxial Thin Films

2531bc1598 Spin qubit shuttling in industry-grade silicon-based quantum processors

2531bd1674 Analogue-Digital Hybrid models of Quantum Computation

2531bd1675 Building the future of chipmaking with smarter etching

2531bd1676 Dopant Spin Qubits for Quantum Computing

2531bd1677 Mapping the Quantum Landscape of 2D Materials, One Atom at a Time

2531bd1678 Mechanochemical feedback during developmental patterning and morphogenesis

2531bd1679 Modular Lateral Flow Assays to Prepare for Disease-X

2531bd1680 Quantum Amplification using Superconducting Nanowires

2531bd1681 Resistive Memories Based on Semiconductor-Insulator Structures for Neuromorphic Computing